Abstract
Descriptions of the six different spinner dolphin (Stenella longirostris) whistle types were developed from a random sample of 600 whistles collected across a 2-yr period from a Fijian spinner dolphin population. An exploratory multivariate visualization suggested an inverse relationship between delta and minimum frequency (58.6%) as well as whistle duration (18.1%) as the most discriminating variables in this dataset. All three of these variables were deemed to be significant when considered jointly in a multivariate analysis of variance (MANOVA): delta frequency (F5594 = 27.167, p < 0.0001), minimum frequency (F5594 = 14.889, p < 0.0001), and duration (F5594 = 24.303, p < 0.0001). Significant differences between at least two of the whistle types were found for all five acoustic parameters in univariate analysis of variation (ANOVA) tests. Constant and sine whistles were found to be the most distinctive whistles, whereas upsweep and downsweep whistles were the most similar. The identification of which parameters differ most markedly between whistle types and the relatively high explanatory power of this study's results provide a logical starting point for objective classification of spinner dolphin whistle types using machine learning techniques.
| Original language | English |
|---|---|
| Pages (from-to) | 1136-1144 |
| Journal | The Journal of the Acoustical Society of America |
| Volume | 148 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 3 Sept 2020 |
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